In this work new artificial learning and innate control mechanisms are proposed for application

in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots)

existent in the literature is enhanced with respect to its capacity of exploring the environment and

avoiding risky configurations (that lead to collisions with obstacles even after learning). The

particular autonomous system is based on modular hierarchical neural networks. Initially,the

autonomous system does not have any knowledge suitable for exploring the environment (and

capture targets œ foraging). After a period of learning,the system generates efficientobstacle

avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous

system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky

configurations) are discussed and the new learning and controltechniques (applied to the

autonomous system) are verified through simulations. It is shown the effectiveness of the

proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and

decrease their probability of appearance in the future and the number of collisions in risky

situations is significantly decreased. Experiments also consider maze environments (with targets

distant from each other) and dynamic environments (with moving objects).